python中的单变量回归 [英] univariate regression in python
问题描述
需要在python的一个数据帧中的列与同一数据帧中的其他几列之间运行多个单因素(单变量)回归模型
Need to run multiple single-factor (univariate) regression models in python between a column in a dataframe and several other columns in the same dataframe
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因此，基于图像，我想在x1& amp;之间运行回归模型. dep，x2&等等等等
so based on the image, i want to run regression models between x1 & dep, x2 & dep and so on and so forth
想要输出-beta，截距，R-sq，p值，SSE，AIC，BIC，残差的正态性检验等
Want to output - beta, intercept, R-sq, p-value, SSE, AIC, BIC, Normality test of residuals etc
推荐答案
您可以在此处使用两个选项.一个是流行的 scikit-learn 库.用途如下
There are two options you can use here. One is the popular scikit-learn library. It is used as follows
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X, y) # where X is your feature data and y is your target
reg.score(X, y) # R^2 value
>>> 0.87
reg.coef_ # slope coeficients
>>> array([1.45, -9.2])
reg.intercept_ # intercept
>>> 6.1723...
您可以使用scikit进行其他统计.
There are not many other statistics you can use with scikit.
另一种选择是 statsmodels ，它提供了远在模型统计信息中更丰富的细节
Another option is statsmodels which offers far richer detail into the statistics of the model
import numpy as np
import statsmodels.api as sm
# generate some synthetic data
nsample = 100
x = np.linspace(0, 10, 100)
X = np.column_stack((x, x**2))
beta = np.array([1, 0.1, 10])
e = np.random.normal(size=nsample)
X = sm.add_constant(X)
y = np.dot(X, beta) + e
# fit the model and get a summary of the statistics
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 1.000
Model: OLS Adj. R-squared: 1.000
Method: Least Squares F-statistic: 4.020e+06
Date: Mon, 08 Jul 2019 Prob (F-statistic): 2.83e-239
Time: 02:07:22 Log-Likelihood: -146.51
No. Observations: 100 AIC: 299.0
Df Residuals: 97 BIC: 306.8
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 1.3423 0.313 4.292 0.000 0.722 1.963
x1 -0.0402 0.145 -0.278 0.781 -0.327 0.247
x2 10.0103 0.014 715.745 0.000 9.982 10.038
==============================================================================
Omnibus: 2.042 Durbin-Watson: 2.274
Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.875
Skew: 0.234 Prob(JB): 0.392
Kurtosis: 2.519 Cond. No. 144.
==============================================================================
您可以看到statsmodels提供了更多详细信息，例如AIC，BIC，t统计等.
You can see that statsmodels offer much more details, such as the AIC, BIC, t-statistics etc.
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